The Deterministic Capacity of Relay Networks with Relay Private Messages

We study the capacity region of a deterministic 4-node network, where 3 nodes can only communicate via the fourth one. However, the fourth node is not merely a relay since it can exchange private messages with all other nodes. This situation resembles the case where a base station relays messages between users and delivers messages between the backbone system and the users. We assume an asymmetric scenario where the channel between any two nodes is not reciprocal. First, an upper bound on the capacity region is obtained based on the notion of single sided genie. Subsequently, we construct an achievable scheme that achieves this upper bound using a superposition of broadcasting node 4 messages and an achievable "detour" scheme for a reduced 3-user relay network.Comment: 3 figures, accepted at ITW 201

The Deterministic Multicast Capacity of 4-Node Relay Networks

In this paper, we completely characterize the deterministic capacity region of a four-node relay network with no direct links between the nodes, where each node communicates with the three other nodes via a relay. Towards this end, we develop an upper bound on the deterministic capacity region, based on the notion of a one-sided genie. To establish achievability, we use the detour schemes that achieve the upper bound by routing specific bits via indirect paths instead of sending them directly.Comment: 5 pages, 2 figures, accepted at ISIT'1

Addressing Class Imbalance for Training a Multi-Task Classifier in the Context of Silk Heritage

Collecting knowledge in the form of databases consisting of images and descriptive texts that represent objects from past centuries is a fundamental part of preserving cultural heritage. In this context, images with known information about depicted artifacts can serve as a source of information for automated methods to complete existing collections. For instance, image classifiers can provide predictions for different object properties (tasks) to semantically enrich collections. A challenge in this context is to train such classifiers given the nature of existing data: Many images do not come along with a class label for all tasks (incomplete samples) and class distributions are commonly imbalanced. In this paper, these challenges are addressed by a multi-task training strategy for a classifier based on a convolutional neural network (SilkNet) that requires images with class labels for the tasks to be learned. The proposed approach can deal with incomplete training examples, while implicitly taking interdependencies between tasks into account. Extensions of the training approach with a focus on hard examples during training as well as the use of an auxiliary feature clustering are developed to counteract problems with class imbalance. Evaluation is conducted based on a dataset consisting of images of historical silk fabrics with labels for five tasks, i.e. silk properties. A comparison of different variants of the classifier shows that the extensions of the training approach significantly improve the classifier's performance; the average F1-score is up to 5.0% larger, where the largest improvements occur with underrepresented classes of a task (up to +14.3%)

Oil spill monitoring using satellite imagery in the Sharm El-Maya Bay of Sharm El-Sheikh, Egypt

Sharm el-Sheikh, in Egypt, is a prominent tourist destination. The city attracts millions of visitors annually due to its exceptional location and pleasant climate. Owing to its natural ecosystem and marine diversity, Sharm El-Maya Bay in Sharm el-Sheikh attracts beachgoers and vacationers. In 1999, however, an oil spill occurred at the site. Previous investigations detected a network of buried steel pipelines and a number of buried reinforced concrete tanks, both of which may have contributed to the contamination problem. Although the problem is so detrimental to health and the environment, no follow-up studies were conducted after 2013. Therefore, the author chose to monitor oil leaks over the headland using frequent, high-resolution Google Earth Pro remote sensing data for the years 2017 to 2022. To disclose whether any corrective measures were taken to address the contamination problem. Moreover, to demonstrate if any unanticipated variations have occurred over many years due to climatic factors. The elucidation of the aforementioned issues demonstrates Google Earth Pro's effectiveness in monitoring pollution problems. The results revealed that the area and perimeter of four oil spots had changed slightly over time. During the specified time period, the standard deviations of the four monitored locations fluctuated between 111.1 m2, 71.6 m2, 83.7 m2, and 254.3 m2. The research proved that the pollution problem has not improved over time because stakeholders have not reacted. In addition, it highlighted the uniqueness of Google Earth Pro in tracking the changes in oil spot size over a time series

Preface: Workshop “Semantics3D - Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences”

[no abstract available

Integrating Motion Priors For End-To-End Attention-Based Multi-Object Tracking

Recent advancements in multi-object tracking (MOT) have heavily relied on object detection models, with attention-based models like DEtection TRansformer (DETR) demonstrating state-of-the-art capabilities. However, the utilization of attention-based detection models in tracking poses a limitation due to their large parameter count, necessitating substantial training data and powerful hardware for parameter estimation. Ignoring this limitation can lead to a loss of valuable temporal information, resulting in decreased tracking performance and increased identity (ID) switches. To address this challenge, we propose a novel framework that directly incorporates motion priors into the tracking attention layer, enabling an end-to-end solution. Our contributions include: I) a novel approach for integrating motion priors into attention-based multi-object tracking models, and II) a specific realisation of this approach using a Kalman filter with a constant velocity assumption as motion prior. Our method was evaluated on the Multi-Object Tracking dataset MOT17, initial results are reported in the paper. Compared to a baseline model without motion prior, we achieve a reduction in the number of ID switches with the new method

Preface: Workshop “Semantics3D - Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences”

[no abstract available

Vehicle Pose and Shape Estimation in UAV Imagery Using a CNN

Vehicle reconstruction from single aerial images is an important but challenging task. In this work, we introduce a new framework based on convolutional neural networks (CNN) that performs monocular detection, pose, shape and type estimation for vehicles in UAV imagery, taking advantage of a strong 3D object model. In the final training phase, all components of the model are trained end-to-end. We present a UAV-based dataset for the evaluation of our model and additionally evaluate it on an augmented version of the Hessingheim benchmark dataset. Our method presents encouraging pose and shape estimation results: Based on images of 3 cm GSD, it achieves median errors of up to 5 cm in position and 3◦ in orientation, and RMS errors of ±7 cm and ±24 cm in planimetry and height, respectively, for keypoints describing the car shape

Bedoi: Benchmarks For Determining Overlapping Images With Photogrammetric Information

For conventional SfM pipeline, image matching is enduring limitation when considering the time efficiency. In the last few years, to speed up image matching procedure, many image retrieval works were proposed to fast find overlapping image pairs, e.g., bag-of-word that clusters hand-crafted local features in a hierarchical way for efficient similar image retrieval, or learning-based global features (such as, VGG or ResNet) are used to represent image in a global compact manner. However, there are rarely benchmarks with referenced overlapping information to: first, evaluate the retrieval performance; second, fine tune deep-learning models along the direction that is more capable to deal with overlapping image pairs. In this work, based on traditional photogrammetric procedures, relevant photogrammetric information is obtained including image orientation parameters, 3D mesh model and etc., we then generate a benchmark for determining Overlapping Images - BeDOI, in which referenced pairwise overlapping relationships are estimated via rigorous photogrammetric geometry. To extend the generality, in total, BeDOI contains 13667 images which are basically UAV and close-range images of various scene categories, e.g., urban cities, campus, village, historical relics, green land, buildings and etc. Lastly, to demonstrate the efficacy of the proposed BeDOI, several image retrieval methods are tested and the experimental results are reported as a competition challenge